Conventionally, there are various types of apparatuses and systems having an image processing mechanism. They include facsimile machines, photocopy reproduction machines, documentation-filing systems, and desk-top publishing systems. These apparatuses and systems perform processing as illustrated in FIG. 1. Images such as photographs, characters, and lines that are recorded on paper-state originals are scanned by a scanner 200, and multivalue image data is thereby generated. A printer 202 performs printing of the data in the following manners. After various types of image processing are performed, the multivalue image data is converted by a binary processor unit 204 into binary black and white pixel data. Then, smoothing processing, for example, image-quality correction, is performed by an image-quality corrector unit 206 of the printer 202; and subsequently, printing thereof is performed by using a printer engine such as a laser-printer mechanism. In the scanner 200, an image sensor, such as a charge-coupled device (CCD), is used to scan the original image. The original image is then converted by a photoelectric converter into an analog signal. Subsequently, processing, for example, white-level control (gain control), shading control, and analog/digital (A/D) conversion are concurrently performed. In addition, correction, for example, MTF (modulus of optical transfer function) correction and gamma correction, are performed to thereby generate multivalue image data that represents pixels each having an 8-bit or greater value level. The multivalue image data thus produced is subjected to binary-coding processing (which hereinbelow will be referred to as binary processing). Thereby, the data is converted into black and white pixel data. The data is then stored in a storage unit, such as a hard disk, and becomes ready for printing upon request. Depending on a request, the data is further processed, and is then printed by the printer 202 according to an area gradation representation method, such as a mesh-point method or a line screen method.
The aforementioned binary processing converts the multivalue image data to black and white pixels in units of a pixel. One of most frequently used binary processing methods is a simple binary processing method. The simple binary processing method converts multivalue image data into white pixels and black pixels on a specific slice level determined corresponding to tones of output images. The simple binary processing method has a function to produce results without causing a problem in regard to the resolution of images such as line images or characters that are clearly defined for black and white colors. However, the method has the problem of not being able to reproduce gradations of halftone images, such as photographs. For this reason, a dither method is instead employed for binary processing to comply with requirements for reproductivity of halftone images, such as photographs. In a dither method, for example, a matrix in the square size of 16×16 pixels corresponding to the primitive pixels is prepared to perform the representation of 256 gradations, and area gradation representation is performed to cause a black and white area to vary spirally from the center corresponding to the gradient values of 0 to 256. However, in the dither method, since the number of gradations is determined according to the size of a matrix, a problem is arisen in that the resolution of a high-contrast line or character image is reduced. In addition, for example, a matrix in the square size of 16×16 pixels is required to obtain 256 gradations. In this case, patchy characters are printed in a normal print mode. In addition, with a matrix of which the size is reduced, gradient characteristics sharply vary to develop defects such as pseudoprofiles, thereby reducing the print quality level.
Other methods conventionally used for reproducing halftone images include matrix area gradation representation methods, such as the aforementioned mesh-point method and line screen method. In the mesh-point method, the sizes of points in a mesh having a predetermined matrix size are controlled to vary corresponding to gradient values. In line screen method, the number of lines in a screen having a predetermined matrix screen is controlled to vary corresponding to gradient values. In these area gradation representation methods, the number of representable gradations is determined by the multiplication product of the number of gradations in a pixel and the matrix size. Therefore, in a printing mechanism such as an electrophotographic printing mechanism in a laser printer, since the pixel size is small, the number of gradations in a pixel is restricted. To avoid the restriction, the matrix size must be large relative to the basic pixel size. However, this arises the problem of reducing the resolution of the print portions of line images and characters. As described above, in the area gradation representation methods, the number of gradations in one pixel greatly depends on the basic pixel size; in proportion to the increase in the resolution, the difficulty increases in the representation of the number of intrapixel gradations through a practically-used printer. For example, the basic pixel size in diameter is 0.042 mm at a basic pixel resolution of 600 dpi. In this case, since the sizes equal to or smaller than toner diameters in a range of 0.006 to 0.010 mm that are used in electrophotography cannot be represented, the greatest representable number of gradations in a pixel is only about 64. In addition, in the high-resolution mesh-point method and line screen method, a load of an image-processing controller unit is increased through processing such as transfer, rotation, compression, and expansion of multivalue image data.
An error-variance method is known as a method that solves the problems that can be caused in the binary processing of the aforementioned multivalue image data, which represents combined images of halftone images (such as photographs) and character and line images. This method is characterized in that the halftone-gradation reproductivity is high, and the reduction in the resolution of portions of line images and character image is relatively low. In addition, in the error-variance method, when multivalue image data does not reach a predetermined slice level, error gradation components are distributed to peripheral pixels according to a variance matrix, and gradations are represented overall in a pseudo-manner. In the error-variance method, high-contrast images such as character images and line images reach a predetermined slice level and are thereby binary-coded. Therefore, the reduction level in resolution is relatively low. However, in binary processing according to the above-described conventional error-variance method, error variance is also performed for profile portions of high-contrast line images and character images. Therefore, in the binary processing of gray-scale fields in the profile portions to which quantization errors are distributed, irregularities are generated in border fields between black and white lines. For specific reference, in binary processing of the gray-scale fields of vertical lines in FIG. 2A and horizontal lines in FIG. 2B, irregularities are generated therebetween. In addition, for thin lines and low-density color lines, since MTF of multivalue image data is low. Therefore, as in the cases of a vertical line in FIG. 3A, a horizontal line in FIG. 3B, and a slanting line in FIG. 3C, defects are caused such that the lines are patchy and discontinuous.
In recent years, as the image-quality corrector unit 206 provided in the printer 202 in FIG. 1, a smoothing mechanism is included to implement the representation of smoothed lines. To avoid the defects, such as irregularities and patchiness, caused in binary black and white pixel data through the error-variance method, it is conceivable that the smoothing mechanism included in the printer is used to implement the representation of smoothed lines. Regarding individual pixels in a binary black and white image as shown in FIG. 4A, the smoothing mechanism precisely controls black and white positions and pixel sizes according to the configuration of peripheral pixels, as shown in FIG. 4B. Thereby, the smoothing mechanism removes jaggy portions caused in slanting lines and characters in profile portions at a resolution of a basic pixel size of a printer. However, the smoothing processing is not effective in the correction of irregularities and patchiness in black and white pixel data binary-coded by the error-variance method. Therefore, the image quality is not improved thereby. In this case, the smoothing processing gives adverse effects. According to the smoothing processing, also for halftone images as in the cases of lines and characters, black and white positions and pixel sizes are precisely controlled according to a peripheral-pixel configuration. As a result, the smoothing processing causes problems in that gradient characteristics are impaired; and reversed gradation, pseudo profiles, and linear and streaky images are generated in halftone images, thereby significantly degrading the image quality level.
Hereinbelow, a description will be made regarding a case in which the error-variance method is used to perform binary processing of a highlight gray image, that is, a high-lightness gray halftone image. In this case, even when an original image has uniformed lightness, error components need to be stored until black pixels are generated through binary processing. As a result, as shown in FIG. 5A, inter-black-pixel intervals are increased. On the other hand, as shown in FIG. 5B, in a gray field where inter-black-pixel intervals are short, the inter-black-pixel intervals are ranged from about 0.04 to 0.08 mm, which are relatively short. Therefore, when the print image is visually observed, individual black pixels cannot be easily identified. That is, as the overall black pixels, gray gradations and resolutions can be secured. However, in a gray field in a highlight portion, as shown in FIG. 5A, inter-black-pixel intervals are increased. Therefore, the individual black pixels can easily be identified in observation through the human eye, and the black pixels are not recognized overall as a gray-scale image. In addition, the density of gray in the highlight portion cannot be easily recognized. That is, the image is recognized to be an image different from the original image.
In the case shown in FIG. 1, scanning is performed by the scanner 200 for the image including photographs, characters, lines, and the like to thereby generate the multivalue image data. In the read (scan) stage, the original image size is required to change corresponding to user-specified scale factors. Therefore, an image-scale varying mechanism is indispensable.
Similarly to an analog photocopy reproducing machine, the image-scale varying mechanism includes a mechanism in which a lens optical system that allows the scale factor of its own to be variable. However, the mechanism is expensive, requires a high cost in precision adjustment and the like. Therefore, in many cases, an electronic image-scale varying mechanism is employed.
A representative example of the scale-varying mechanism is of a coordinate conversion method. The coordinate conversion method converts multivalue levels of all pixels into those corresponding to absolute distances of logical pixel positions. The conversion is performed on planes in a primary scan direction and a secondary scan direction corresponding to scale factors. The method is excellent at gradation reproductivity. However, the method has disadvantages in that processing is significantly complicated, and the resolution is significantly reduced when a document, such as an office document, having containing high-contrast images is processed.
A pixel-removal method (which hereinbelow may be simply referred to as a “removal method”) is used most popularly in recent years. In this method, a multivalue image signal is once subjected to interpolation processing in a primary scan direction and a secondary scan direction to thereby increase the size of an image up to a size of the image represented by a required number of pixels, and thereafter, the image size is reduced by pixel-removal to a required size. In this method, pixels to be removed are either determined from a fixed pattern based on a scale-reduction factor (which hereinbelow will simply be referred to as a “reduction factor”), or are determined from reduction factors through calculation.
At this time, pixels are removed in units of a line in the primary scan direction and the secondary scan direction. Therefore, for an image of a character, a slanting line, or the like, image defects, such as streaky indented portions and aberrant portions, and the like become conspicuous. An additional disadvantage is that one thin line to be represented can be omitted because of the line-unit removal, and there may even be a terrible case where represented characters cannot be readable. In this connection, there has been proposed a method for removing pixels at random. Also in this case, however, patchiness in thin lines and irregularities caused by flickered lines appear noticeably on an output image. In addition, to prevent the omission of thin lines, there has been proposed a method for determining removal pixels either after binary processing or according to peripheral-pixel patterns created using values binary-coded with dummy values.